Age reading of sardine scale using neural network

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1. A personal-computer-based system was developed for counting annual circuli in scales of Japanese sardine using artificial neural network. The image processing was used to digitize the video image of a scale and store the light intensity from five radial transects. The values and positions of the peaks of the intensity along the transect were determined and normalized to input to the neural network as 5^<**>40 mesh two-dimensional data.2. Performance of age determination of the system was examined using a data set from 20 samples of sardine scales. In the learning, the neural network showed learning ability to determine age from training samples without error. However, the performance of the trained neural network for test samples got no further than 50% of correct recognition rate, while error rate was low.3. The followings remain to be examined : (1) much more learning is needed as the number of learning data set were too small, (2) there is room for improvement of illumination to get better image with clear annual circuli, (3) since radial grooves, which have higher contrast than annual circuli in the scale image prevent counting annual circuli correctly, some pre-processing procedure of the image is required to eliminate the noisy grooves, and (4) input data to the neural network should be examined further for larger number of measureing transect and/or larger mesh size than 5^<**>40, which increase resolution power on the one side and the number of units in the neural network on the other hand, i.e. much more calculating time.